unitn.it/composes/ (a) 2L direct (b) 2L interaction (c) 2L interaction direct (d) 1L direct (e) 1L interaction (f) 1L interaction direct  We take DSM vectors as input, since they provide us with semantically rich word representations, and seek to induce a compatibility measure by learning the parameters of a model in a supervised manner.

we used the word vectors publicly available at http://clic.cimec.unitn.


The retrieval step is then carried out by locating the last pair of the first n% of the pairs of similar words in the sorted list of scores and determining the percentage of similar word pairs in the sub-list delimited by the last pair of similar words.

the procedure treats similar word pairs as successful retrievals and determines the accuracy rate when the recall rate is n%.


This paper solves the inconsistence by normalizing the word vectors.

we enforce the word vectors to be in a unit length during the learning of the embedding.


The resulting 6000 English-Spanish word pairs are used to train and test the bilingual transform in the way of cross validation.

the 6000 pairs are randomly divided into 10 subsets, and at each time, 9 subsets are used for training and the rest 1 subset for testing.


The main purpose of preordering is to find a better translation performance in fast decoding conditions.

by preordering the text we expect to be able to decode with less distortion or phrase reordering, resulting in faster decoding.


(2011), Lee and Lin (2014)), described as follows.

for each sentence x s , we define the set of preference pairs as the set of ordered translation pairs for which the evaluation score prefers the first element: following Lee and Lin (2014), we define the loss (or, error) of any sentence s as the sum of its pairwise squared hinge losses: no loss is contributed by preference pairs for which the classification score correctly prefers the first element by a large-enough margin, i.e., h s i ≥ h s j +1; all other preference pairs contribute some loss.


Here, we see a consistent improvement upon the maxsuff -strategy (AFH14) that simply picks the longest matching suffix among the base forms seen and assigns the unseen word to the same paradigm (breaking ties by paradigm frequency), as well as improvement over other learning strategies (D&DN13).

marked is the improved accuracy on German verbs.


An obvious extension to the current method is to inspect a suggested reconstructed table holistically, i.e., not relying only on base form features.

one could avoid making a commitment to a particular paradigm based solely on the features of the base form, and instead also include features from all the forms that a paradigm would generate.


2 Finally, we parameterize the frequency of each language in the text.

for each language , a multinomial parameter θ specifies the probability of transitioning to when you draw a new language.


We leverage both general and domain specific query reformulations for this purpose.

we use biomedical concepts, ontology information, keyphrases and the syntactic structure of the citance.


We consider a statistical measure for identifying key terms in the citance.

we computed the idf 2 of the terms in the citance in a domain-specific corpus to evaluate their importance.


For this reason, we also considered ROUGE-L (Lin, 2004) as another evalua-tion metric, as it rewards a method for retrieving spans that are similar to the gold spans.

rOUGE-L, takes into account the sentence similarity by considering the longest in sequence n-grams between the retrieved spans and gold spans.


Our implementation of the reference-based approach ("ref" in §4) uses SVR to estimate a model to predict human scores from various measures of the similarity between the response and information from the scoring guidelines provided to the human scorers.

we use the following information from §2: (a) sentences expressing key concepts that should be present in correct responses, and (b) small sets of exemplar responses for each score level.


The WordNet and word2vec alignment metrics are computed as follows, where S is a student response, R is one of a set of reference texts, W s and W r are content words in S and R, respectively, and   When R is one of a set of reference texts (e.g., one of multiple exemplars available for a given score point), we use the maximum similarity over available values of R. In our data, there are multiple exemplars per score point, but only one text (usually, a single sentence) per key concept.

we select the most similar exemplar response for each score level.


8 For training our stacking model, we first train the response-based regression model (SVR #1 in Figure 1), and then train the reference-based regression model (SVR #2) with an additional prediction feature value from the response-based model.

the lower-layer model concentrates sparse and binary features into a single continuous value, which accords with reference-based dense features in the upper-layer model.


In this paper we explore whether current NLP technology can be used to address some of these tasks.

we focus on script summarization, which we conceptualize as the process of generating a shorter version of a screenplay, ideally encapsulating its most informative scenes.


The diversity of chain S is the sum of the diversities of its successive scenes: The diversity d(s i , s i+1 ) of two scenes s i and s i+1 is estimated taking into account two factors: (a) do they have any characters in common, and (b) does the sentiment change from one scene to the next: respectively denote character and sentiment similarity between scenes.

d char (s i , s i+1 ) is the relative character overlap between scenes s i and s i+1 : d char will be 0 if two scenes share the same characters and 1 if no characters are shared.


Instead, we used distant supervision based on Wikipedia to automatically generate a gold standard.

we assume that Wikipedia plots are representative of the most important content in a movie.


We observe that SceneSum summaries are overall more informative compared to those created by the baselines.

aMT participants are able to answer more questions regarding the story of the movie when reading SceneSum summaries.


Constraints are required to ensure that the selected nodes and edges form a valid graph.

if an edge (i, j) is selected (e i,j takes value of 1), then both its endpoints i, j must be included: Connectivity is enforced using a set of singlecommodity flow variables f i,j , each taking a nonnegative integral value, representing the flow from node i to j.


We wish to incorporate semantic relatedness between different entities into existing models to tackle the problem described above.

we propose to capture such semantic relatedness between different entities with world knowledge represented as triples, e.g., Gates-create-Microsoft.


Therefore, we can model text coherence by measuring how mentions of various entities are distributed within the text.

for a given document d, an entity grid is constructed in which the rows represent the sentences and the columns represent entities.


Both sources are presented in triples, argument 1 -predicate-argument 2 , (e.g., Gates-create-Microsoft), where the two arguments are usually entities and the predicate is the relation between them (Zhang et al., 2014).

knowledge selection For each document d, we then select the subset of relevant knowledge instances, in the sense that they represent relations between the entities in d. we extract all entities in d, and query the knowledge bases to obtain all the knowledge instances in which both of the two arguments, argument 1 and argument 2 , match some of the entities in d. One issue in knowledge selection is whether to retrieve knowledge instances using exact or partial matching.


Inspired by the original entity-based model, in which local coherence is reflected by the patterns of how entities act grammatically from one sentence to the next, we believe that local coherence can also be characterized by the patterns of how world knowledge relates a pair of sentences.

given a set of sentences, there are different patterns of how knowledge instances are distributed among them.


We then use the frequencies of these distribution patterns over the entire document as additional features into the entity-based model.

for each particular distribution pattern b k , its corresponding , where |b k | is the number of occurrences of b k in the sentence graph.


Since text coherence is a relative concept rather than a binary distinction, in both tasks, we formulate the problem as pairwise preference ranking.

given a set of texts with different degrees of coherence, we train a ranker to prefer the more coherent text over the less coherent one.


with world knowledge Moreover, we also observe that the document length has a non-universal effect, in terms of how the model could benefit from incorporating world knowledge.

we find that world knowledge has a greater effect on short documents, as demonstrated in Table 2.


In this paper, for the evaluation of text coherence, we go beyond strict entity matching and model the semantic relatedness between distinct entities through the use of world knowledge.

we incorporate world knowledge into two existing frameworks: (1) the unsupervised graph-based model (G&S), and (2) the supervised entity-grid model (B&L).


In our future work, we wish to explore the effect of our world knowledge in conjunction with discourse relations.

we plan to incorporate world knowledge into the framework of discourse role matrix (Lin et al., 2011;Feng et al., 2014).


However, identifying potential arguments (which is typically performed by an entity extraction system), linking arguments to their event mentions (which is typically performed by an event extraction system), and determining the compatibility between two event arguments (which is the job of an entity coreference resolver), are all non-trivial tasks.

end-to-end event coreference resolution is complicated in part by the fact that an event coreference resolver has to rely on the noisy outputs produced by its upstream components in the standard information extraction (IE) pipeline.


While the lack of comparable lexical knowledge bases in Chinese can be mitigated in part by the use of event coreference annotated data, we focus on a challenging version of the task ---unsupervised Chinese event coreference resolution.

our goal is to learn an event coreference model without using data annotated with event coreference links.


In ACE 2005, a set of argument roles is defined for each event type.

an event's type determines what roles its mentions' arguments can assume.


This example contains three event mentions belonging to the ACE event types.

these three mentions are triggered by the words 离 (leaving), 暗杀 (assassinated) and 攻击 (attack).


Owing to the presence of hidden data, we estimate the model parameters using the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).

we use EM to iteratively estimate the model parameters from data in which each event mention is labeled with the probability that it corefers with each of its candidate antecedents, and apply the resulting model to relabel each event mention with the probability that it corefers with each of its candidate antecedents.


Given P (l=1|e, k, c), the goal of the M-step is to (re)estimate two of the four groups of parameters mentioned above, namely P (e t |c t , l=1) and P (f i c |l), using maximum likelihood estimation.

p (e t |c t , l=1) is estimated as follows: where Count(c t , l=1) is the expected number of times c has trigger word c t when it is the antecedent of an event mention; and |t| is the number of possible trigger words in the training data (we treat the "trigger word" of a dummy candidate antecedent as an unseen word).


To start the induction process, we initialize all parameters with uniform values.

p (e t |c t , l=1) is set to 1 |t| , and p (l=1|k c ) is set to 0.5.


After training, we can apply the resulting model to resolve event mentions.

given an event mention e, we determine its antecedent as follows: where C is the set of candidate antecedents of e. we apply Equation 11to each of e's candidate antecedents, and select the one that yields the largest probability.


More specifically, to extract the event mentions used in our evaluation, we employ SinoCoreferencer 3 , which, as mentioned before, is an end-to-end ACE-style Chinese IE system that achieves state-ofthe-art event coreference results.

the event triggers needed to compute the trigger-based context features are extracted using SinoCoreferencer's event extraction subsystem.


To investigate the contribution of each probability term to overall performance, we conduct ablation experiments.

in each ablation experiment, we remove exactly one term from the model and retrain it.


First, to gain insights into the differences between the two resolvers, we examine those candidate event mentions that are correctly handled by one model but not the other (Section 6.4.1).

we consider a candidate event mention e correctly handled if (1) e is a correctly resolved anaphoric event mention; (2) e is an unresolved singleton event mention; or (3) e is an unresolved non-event mention (i.e., not a true event  mention).


This is perhaps not surprising given the unsupervised model's relatively stricter conditions on resolving a candidate event mention.

it is unlikely to posit two candidate event mentions as coreferent unless (1) their triggers have a BV match or a large word2vec similarity value and (2) none of the non-coreference conditions are satisfied.


Not only does the unsupervised model perform much better in not resolving singletons and nonevent mentions, but it is also slightly more accurate in resolving the anaphoric event mentions, which ultimately enables it to achieve a higher recall than SinoCoreferencer.

it correctly resolves 50 anaphoric mentions that are incorrectly handled by SinoCoreferencer.


Neither of these two recall-enhancing features of SinoCoreferencer is a precise indicator of event coreference.

employing them widens the precision gap between the two resolvers.


Unfortunately, populating a universal schema knowledge base using matrix factorization suffers from a problem all distantly-supervised techniques share: you can only reliably learn relations that appear frequently enough in the knowledge base.

for relations that do not appear in the knowledge base or for which no facts are known we cannot learn a predictor at all.


More precisely, let v (•) denote the mapping from constant-pairs and predicates to their corresponding embedding.

v rm is the embedding for predicate r m , and v (e i ,e j ) is the embedding for the pair of constants (e i , e j ).


Assuming a finite domain, such a formula can be unrolled into a conjunction of propositional statements of the form F ij = r s (e i , e j ) ⇒ r t (e i , e j ), one for each entity-pair (e i , e j ) in the domain.

[F] = (e i ,e j )∈P [F ij ], and therefore L([F]) = (e i ,e j )∈P L([F ij ]).


(2013) by sampling unobserved facts that we assume to be false.

in every epoch and for every true training fact r m (e i , e j ) we sample an (e p , e q ) such that r m (e p , e q ) is unobserved.


Since logical connections between relations are modeled explicitly, such approaches are generally hard to scale.

approaches based on Markov Logic Networks (MLNs) (Richardson and Domingos, 2006) encode logical knowledge in dense, loopy graphical models, making structure learning, parameter estimation, and inference hard for the scale of our data.


In this paper, we show that a deeper semantic knowledge representation -including the Abstract Meaning Representation (AMR) (Banarescu et al., 2013) -can capture contextual properties that are discriminative enough to disambiguate entity mentions that current state-of-the-art systems cannot handle, without the need for EL training data.

for a given entity mention, using AMR provides a rich context representation, facilitating the selection of an optimal set of collaborator entity mentions, i.e., those co-occurring mentions most useful for disambiguation.


We use AMR to represent semantic information about entity mentions expressed in their textual context.

given an entity mention m, we use a rule based method to construct a Knowledge Network, which is a star-shaped graph with m at the hub, with leaf nodes obtained from entity mentions reachable by AMR graph traversal from m, as well as AMR node attributes such as entity type.


Another related field aims to learn continuous vector representations for various abstraction levels of natural language.

the creation of socalled word embeddings has attracted a lot of attention in the past years, often by implementing neuralnetwork language models.


Instead of reducing and pruning possible feature concatenations (e.g., by manual feature template construction as in the traditional approach), this tensor scoring method avoids parameter explosion and overfitting by assuming a low-rank factorization of the parameters A.

a is decomposed into the sum of k simple rank-1 components, Here k is a small constant, P, Q ∈ R k×n , R ∈ R k×m and S ∈ R k×l are parameter matrices, and P (i) (and similarly Q(i), R(i) and S(i)) represents the i-th row vector of matrix P .


Following standard practice, we optimize the parameter values in a maximum soft-margin framework.

for the given sentencex and the corresponding syntactic treeŷ syn , we adjust parameter values to separate gold semantic parse and other incorrect alternatives: where Z(x,ŷ syn ) represent the set of all possible semantic parses, and cost(ẑ sem , z sem ) is a non-negative function representing the structural difference betweenẑ sem and z sem .


This requires "costaugmented decoding" to find the maximum violation with respect to the gold semantic parse: When the constraint (4) is violated (i.e.z sem = z sem ), we seek a parameter update ∆θ to fix this violation.

we define the hinge loss for this example as follows, and we revise the parameter values to minimize this loss function.


We can train our model using only the manual feature set and then use the corresponding feature weights w to intialize the tensor.

we create a sparse tensor T ∈ R n×n×m×l by putting each parameter weight in w into its corresponding entry in T .


By doing so we avoid the costly and error-prone manual annotation of temporal relations by using the textual order of recipes to approximate their temporal order.

1 we address the cooking recipes domain, which we motivate in §2.


We combine both objectives by directly modeling the probability of a document d e in target language e given a query q f in source language f , factorized as follows: Applying the same Viterbi approximation during inference as in (1), we choose the retrieval score of d e to be the score of the highest scoring hypothesis h, where the product between both models can be interpreted as a conjunctive operation similar to a product of experts (Hinton, 2002): A high score is achieved if both experts, namely translation and retrieval models, assign high scores to a hypothesis.

the model attempts to produce a wellformed translation, but at the same time chooses lexical items present in the bag-of-words representation of the document.


The joint scoring model is defined as follows: where the weight vector is defined by the vector concatenation w = w smt w ir , and q e refers to the yield that is determined uniquely by derivation h. Following the interpretation of our joint model as forced or constrained decoding, we can view pipeline approaches such as the direct translation baseline as instances of unconstrained decoding.

the SMT decoder yields a single translation output for every document and the assignment of document scores is deferred to a (monolingual) retrieval model given this single output structure.


For a certain fact type, we choose the SF system which has the best performance for comparison.

we compare with two successful approaches: (1) the combination of distant supervision and rules (e.g., (Grishman, 2013;); (2) patterns based on dependency paths (e.g., Yu et al., 2013)).


To improve word alignment and MT in a lowresource setting, we design a multitask learning approach that utilizes parallel data of a third language, called the pivot language ( §3).

we derive an efficient and easy-to-implement MAP-EM-like algorithm that jointly trains sourcetarget, source-pivot and pivot-target alignment models, each on its own bitext, such that each model benefits from observations made by the other two.


This combination process is referred to as triangulation (see §5).

they construct the triangulated source-target t-table t st from the source-pivot and pivot-target t-tables t sp , t pt using the following approximation: Subsequently, if a source-target corpus is available, they train a standard source-target model Θ st , and tune the interpolation with respect to λ interp to reduce alignment error rate (Koehn, 2005) over a hand-aligned development set.


1) by using it to construct an informative prior for the source-target t-table t st ∈ Θ st .

we modify the word alignment generative story by placing Dirichlet priors on each of the multinomial t-table distributions t st (• | s): Here, each α s = (.


1 and the exponentiated pointwise mutual information (PMI), derived from simple token co-occurrence statistics of the source-target bitext.

we adjust: and normalize the result to form valid conditional distributions.


Both our methods outperform the baseline and the interpolation approach.

the joint training approach more than doubles the gains obtained by the interpolation approach, on both F-and Bleu.


We argue that our joint training procedure can be seen as optimizing the posterior likelihood of the three models.

suppose we place Dirichlet priors on each of the t-tables t st , t sp , t pt as before, but define the prior parameterization using a single hyperparameter α = {α spt } and its marginals such that: Intuitively, α spt represents the number of times a source-pivot-target triplet (s, p, t) was observed.


Knowledge incorporation can solve the content sparsity problem effectively for topic modeling.

the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the phrases, to guide the generative process of short text.


While constituency-based parsing seems to be a reasonable choice for compositionality in general, it may not be the best choice for all NLP tasks.

for relation classification, one may prefer to use a structure that encodes more information about the relations between the words in a sentence.


Section 3 presents the algorithm along an example.

section 3.2 gives the algorithms for automaton and parse table constructions, and section 3.3 presents the parsing algorithm.


For languages where no such normalization dictionary is available, we use word clusterings based on Brown clusters (Brown et al., 1992) to generalize tags from unambiguous words to previously unseen words in the same class.

to extend the dictionary D to D using clusters, we first run clustering on the unlabeled data T , using Brown clustering.


In calculation, we simply apply logarithm since: 3.6 Summary Generation As three scores above including the representativeness, diversity and length constraint are measured in a unified sentence scoring model, generating a summary with out method is basically achieved by selecting the higher ranking sentences.

our summary contains more representative and diversified information in the limited length.


In this paper, we have investigated the feasibility of rapidly bootstrapping semantic annotation tools for new target languages 2 by mapping an existing semantic lexicon and software architecture.

we tested the possibility of automatically translating existing English semantic lexicons into other languages, Italian, Chinese and Brazilian Portuguese in this particular case.


Also, we feel that, the hierarchy depends on the task or the context.

one person could as-sume different roles in different context.


This is explained by the fact that the Skip-gram model subsamples context words, varying the size of the window size stochastically, so that words closer to the center word are sampled more frequently.

when defining a window size of 5, the actual window size used for each sample is a random value between 1 and 5.


We will therefore determine the size of each sense instead of each word.

we determine the size of each noun synset in the Japanese WordNet (Bond et al., 2009).


We can obtain such part-of (holonymy) relations from the WordNet.

for each physical object obtained in Section 3.1, we retrieve its holonymy synsets.


we search an n-gram corpus (Kudo and Kazawa, 2007) for the textual patterns including "A is longer than B".


In order to produce the transformed signal, we simply predict frame-by-frame.

for a frame x of the input we predict a transformed frameŷ as the mode of density p(y|x), which we estimate from training data.


LLR is a non-parametric method that estimates p(y|x) as a linear transformation that varies slowly with the input x.

p(y|x) is estimated as follows: The transformationÂ is a function of x, and is computed by solving a weighted least squares problem that depends on x. w is a kernel function that measures similarity between the current input, x, and each of the source training frames, x i .


We use dynamic time warping to induce the frame alignment.

we find the minimum cost monotonic alignment from source frames into target frames where the cost of each alignment edge is the L2 distance between the corresponding vectors.


We show that in order to produce helpful feedback for responsebased learning, parser performance on incorrect En-glish queries needs to be taken into account, which is standardly ignored in parser evaluation.

for the purpose of parsing translated queries, a parser should retrieve correct answers for correct English queries (true positives), and must not retrieve correct answers for incorrect translations (false positives).


e) HEADPOSD, i.e., HEADPOS, plus direction.

h 1 , l 1 and h 2 , l 2 count as disagree- Each factorization yields a symmetric confusion matrix.


The values for our cost-sensitive systems (LABEL, LABELD, CHILDPOSD, HEADPOS, HEADPOSD) are never above 1, which means that we are selectively underfitting the parser for specific syntactic phenomena.

we use the doubly-annotated data to regularize our model, hopefully preventing overfitting to annotators' biases.


It is not simply a backoff from network information to textual information in the sense that it propagates the LR geolocations through the network.

if a test node is disconnected from the training nodes but still has connections to other test nodes, the geolocation of the node is adjusted and propagated through the network.


See two possible contexts for noche and fortuna in the examples below: era una noche oscura de ('it was a dark night of') de probar fortuna en el ('to try fortune in the') Third, we define the complexity of a word using the relative frequency of the synonyms within the same sense in the List of Senses.


We later show how this percentage changes with smaller k. Finally, for each complex word and the contexts it appears in, we select as simpler synonym the most frequent synonym of the sense that appears most frequently for the n-gram corresponding to that (word, context) pair.

to disambiguate the sense, our method uses the context where the target word appears.


For the complexity part we use the same as our new method.

both algorithms consider the same words as complex.


We then pass over this mixed corpus, and for each word w, if {w | w, w ∈ R ∼ } is non-empty and of cardinality k, i.e., w is in the seed list of equivalences, we replace w with w with probability 1/2k.

we flip a coin whether to replace w, and then randomly choose one of its equivalences as our replacement.


We treat the task of sentiment analysis as a supervised learning problem, where we are given labeled data {(x i , y i )} n i=1 and the goal is to estimate a decision function f (x) → y that maps input examples to labels.

we use a linear SVM model with the prediction function of the following form: f = sign(w T x + b), where the model weights w are estimated from the training set.


As the number of unique unigrams and bigrams can be very large and we would like to keep our sentiment lexicon rea-  sonably small, we filter entries with small weights.

we found that selecting items with a weight greater than 1e − 6 did not cause any drop in accuracy, while the resulting lexicon is reasonably compact -it contains about 3 million entries.


Recently, deep convolutional neural networks for sentence modelling (Kalchbrenner et al., 2014;Kim, 2014) have shown promising results on several NLP tasks.

(Tang et al., 2014) showed that learning sentiment-specific word embeddings and using them as features can boost the accuracy of existing sentiment classifiers.


But we can not simply replace top by st, since argmax can also be applied in other scenarios such as "the largest city".

argmax is a polymorphic function, and to assign a correct type for it we have to introduce type variables: where type variable 'a is a place-holder for "any type".


Let p ij and p ij be the values of the i'th property in the j'th slot in p, p (e.g., for a second order sibling part as in Figure 1, p 1,4 will correspond to the label of the edge e2 in p) , and let C p↔p ∈ {0, 1} m×s be a binary matrix comparing p and p such that C p↔p ij = 1 when p ij = p ij and 0 otherwise.

simple algebra yields that: calculating k (p, p ) amounts to multiplying the sums of the columns in C. 3 The runtime of where |y 1 | , |y 2 | are the number of parts in y 1 and y 2 .


We get the following constrained optimization program: The mix constraints make sure that the solution is nontrivial.

a very large number of words are monosemous, and the procedure will leave the embeddings of these words unchanged.


In this work we propose to encode the meaning of words using the structural information in knowledge bases.

instead of modeling the meaning based on the co-occurrences of words in corpora, we model the meaning based on random walks over the knowledge base.


No standard dataset exists for where OOV lemmas should be attached to WordNet; therefore in the first evaluation, we assess construction accuracy by simulating the inclusion of OOV lemmas using those already in WordNet, which allows testing on tens of thousands of lemmas.

the CROWN attachment approach is used to reattach all monosemous lemmas in WordNet.


The second document F 2 is a news article about terror, and is labeled as "not foreign policy".

the results shown in table 2, confirm our intuition, 9 the point where precision and recall are equal.


Participants were then asked to re-read the article and highlight any words or phrases they believed were related to framing.

participants were given the following prompt and instructions: Political issues can often be complex, contentious, and difficult to understand.


That said, examining the results more closely exposes that, in order to achieve this level of performance, the classifier makes a trade-off.

the classifier is far more aggressive than humans, resulting in significantly higher recall but lower precision.


To assess whether the CCDFs of the several types of texts we take into account show significant differences, we use the Kolmogorov-Smirnov goodnessof-fit test, which specifically targets cumulative distribution functions.

for each phonetic device and dataset, we use a two-tailed Kolmogorov-Smirnov test (again with Bonferroni correction) to test whether the number of examples above the threshold is higher in the persuasive sentences than in their non-persuasive counterparts for that device.


The experiments that evaluated models on individual frames (Section 4.3) from the video have trends similar to those seen on mean pooled frame features.

the model trained on Flickr30k, when directly evaluated on YouTube video frames performs better on subjects and verbs, whereas the one trained on COCO does better on objects.


The term ((card(S) + 1) − k) is inversely proportional to k, so its highest value is when k is 1.

the value of the term is maximum when the selected objects are ranked first.


Using the rules presented in Table 1 we create visual encodings for them (see Table 2, source side), and finally translate them into natural language using a Phrase-based SMT engine (Koehn et al., 2003).

given a source visual expression f, our aim is to find an equivalent target natural language descriptionê that maximizes the posterior probability: Most recent SMT work models the posterior P(e|f) directly  using a log-linear combination of several models where: and the decision decision rule is given by: where h k (f, e) is a scoring function representing important features for the translation of f into e. Examples include the language model of the target language, a reordering model, or several translation models.


The results of our human evaluation study are shown in Table 5.

we show, proportionally, how often our participants ranked each system 1st, 2nd and so on.


This allowed us to perform Analysis of Variance (ANOVA) which revealed a reliable effect of system type.

post-hoc Tukey tests showed that our SMT model is significantly (p < 0.01) better than the other two comparison systems but does not differ significantly from the human goldstandard.


We explore linear projections that map continuous representations of rare foreign phrases to English phrases.

we propose to learn many local projections that are specific to a given foreign phrase.


1 We therefore decided to use off-the-shelf word representations to build phrase vectors.

we chose the continuous bag-of-words model (Mikolov et al., 2013b) which is very fast to train and scales very well to large monolingual corpora.


We therefore propose to learn many local linear projections which are individually trained for each unlabeled source phrase.

for each unlabeled source phrase f , we learn a mapping W f ∈ R d×d based on the translations of m of f 's labeled neighbors: Compared to the global projection, we require an additional k-NN query to find the labeled neighbors for each unlabeled source phrase.


We therefore investigate approximated k-NN query methods which sacrifice some accuracy for a large gain in speed.

we look into locality sensitive hashing (LSH; §4.1), a popular method, as well as redundant bit vectors (RBV; §4.2), which to our knowledge has not been previously used for natural language processing tasks.


singletons and strong MWEs may receive labels (subject to a POS filter).


BCF jointly learns categories, feature types, and their associations.

it infers one global set of feature types which is shared across categories (e.g., ANIMALS and VEHICLES can be described in terms of colors).


It learns a set of concept clusters (i.e., categories), as well as a clustering over features (i.e., feature types), and a distribution over those feature clusters for each category (i.e., category-feature type associations).

the occurrences of a concept will be assigned a category, based on how similar the concept's feature types are compared to the feature types of all other potential categories.


If the model has acquired informative features, they will be predictive of the unknown concept.

the model is presented with a set of previously unseen test stimuli with the target concept removed.


On this view, the use of natural languages is assumed to follow optimal information transmission results from information theory (Shannon, 1948).

speakers should structure their messages to approximate uniform information density across symbols (words and phonemes), which is optimal for transmission of information through a noisy channel.


First, we estimated language models with domain-specific corpora.

for tweets from each game we used a training corpus consisting of the tweets from all the other games.


We predicted that we would see similar developments in information structure as in more traditional conversational settings, even though there was no formal conversation or explicit linguistic history to develop common ground.

we predicted that the build-up of contextual information would cause the context-independent per-word entropy to rise over time, replicating the effect that has been observed across languages and genres (Genzel and Charniak, 2003;Qian and Jaeger, 2012).


5 We used mixed-effects linear regression to quantify this relationship, using the time of an at-bat to predict both per-word and per-tweet entropy during the at-bat.

we used the logarithm of time as our fixed-effect predictor, per the contextdecay models of Qian and Jaeger 2012.


For the purposes of this evaluation, total PCFG surprisal (Hale, 2001;Levy, 2008;Roark et al., 2009) will be used as a measure of hierarchic syntactic processing.

pCFG surprisal will be calculated using the van Schijndel et al.


The results of this evaluation suggest that reading times are mostly affected by local hierarchic structure, but the fact that GCG PCFG surprisal is able to provide a significant fit even in the presence of the PTB PCFG surprisal predictor suggests that some non-local information affects reading times.

while this evaluation showed that accumulated syntactic context is not generally a good predictor of reading times, some or all of the non-local information contained in the GCG categories is used by readers and so influences reading time durations over the local structural information reflected in the PTB PCFG surprisal measure.


We perform a quantitative evaluation of this clustering through an intrusion task (Chang et al., 2009).

we show individual raters three terms, selected so that two terms are from the same cluster, and the third term is from the other cluster; we then ask them to identify which term is least like the other two.


Knight, 2004;Cowan and Collins, 2006;Chiang, 2010) has been somewhat less popular than treeto-string translation, we believe there are many benefits of considering target-side syntax.

reordering can be defined naturally with non-terminals in the target-side grammar.


3 Selection based on n-Gram Frequency 3.1 Sentence Selection using n-Gram Frequency The first traditional method that we cover is a sentence selection method.

it selects the sentence including the most frequent uncovered phrase with a length of up to n words in the source language data.


However, there are still a number of avenues for improvement.

as the proposed method selected segments that took more time to translate due to technical terms, the combination with methods to harvest unknown words (Daumé III and Jagarlamudi, 2011) or optimize the selected segments based on the time required (Sperber et al., 2014) is potentially useful.


But NCE-U performed worse than we expected.

the differences between NCE-U and NCE-T are larger than those reported by Zhang et al.


reviews for a movie, or arguments for a controversial social issue), and then outputs a one-sentence abstractive summary that describes the opinion consensus of the input.

we investigate our abstract generation model on two types of opinionated text: movie reviews and arguments on controversial topics.


During training, we use these scores to tune model parameters, and during testing, we predict the sentence importance scores given the features in other columns.

we learn the embedding of important sentences.


In the second part of this experiment we directly compare the knowledge acquisition of these workers.

we ask a different set of workers to evaluate the summaries created by the partnered workers.


We chose hyper-parameters based on a grid search and picked the one which gave the best perplexity on the validation set.

we searched over the number of hidden units H of the recurrent layer, the learning rate η, the learning rate annealing schedule γ (the factor by which to decrease η if the validation perplexity increases), and the gradient clipping threshold κ.


For the sake of completeness we also compare our models to the recently proposed standard Neural Machine Translation (NMT) systems.

we compare to a smaller re-implementation of the attentive stacked LSTM encoder-decoder of Luong et al.


But given that our method can produce non-treelike graphs, we take a first step towards the prediction of CDUs as part of discourse structure by encoding them in a hypergraph-like framework.

we will transform our corpus by distributing relations on CDUs over all their constituents as we describe in section 3.


The correct SDRS for (2) is one in which both b and c together explain why the French economy continues to suffer.

b and c form a CDU and give rise to the following graph: HR on (2) produces a graph whose strictly compositional interpretation would be false-b alone explains why the French economy continues to suffer.


For example, we could complicate (2) slightly: ( In 3, the SDRS graph would be: However, this SDRS entails that a is explained by [c, d] and that b is explained by [c, d].

eX-PLANATION "distributes" to the left but not to the right.


As decoding proceeds, the influence of the initial input on decoding (i.e., the source sentence representation) diminishes as additional previously-predicted words are encoded in the vector representations.

4 the first words to be predicted significantly determine the remainder of the sentence.


Although this method can benefit from parameter sharing of the LM part of the network, the parameters of similar input slot-value pairs are not shared 4 .

realisation of any unseen slot-value pair in the target domain can only be learned from scratch.


We use this approach in a case study of Oxfordstyle debates-a competitive format where the winner is determined by audience votes-and show how the outcome of a debate depends on aspects of conversational flow.

we find that winners tend to make better use of a debate's interactive component than losers, by actively pursuing their opponents' points rather than promoting their own ideas over the course of the conversation.


2008in the context of U.S. Senate speeches.

we estimate the divergence between the two sides' word-usage in the introduction, where word-usage is modeled as multinomial distributions smoothed with a uniform Dirichlet prior, and divergence is given by log-odds ratio.


For instance, by using a basic unigrambased definition of discussion points, we do not account for the context or semantic sense in which these points occur.

our annotators found that a significant proportion of the discussion points under our definition actually referred to differing ideas in the various contexts in which they appeared.


cheaper and pricey) is critical for the performance of dialogue systems.

a dialogue system can be led seriously astray by false synonyms.


To tackle this problem, we have developed an approach to jointly process language and vision by incorporating semantic role information.

we use a benchmark dataset (TACoS) which consists of parallel video and language descriptions in a complex cooking domain (Regneri et al., 2013) in our investigation.


The CRF structure is created based on information extracted from language.

more s 1 , ..., s 6 refers to the observed text and its semantic role.


It measures the similarity between distance histograms.

histograms of distance values between the tracks of the predicate and other roles for each verb are first extracted from the training video clips.


In metaphorical language, however, a meaning transfer takes place and this is no longer the case.

in visual data, where no linguistic conventionality and stylistic effects take place, PHRASCOS1 captures this property.


More specifically, we consider scenarios where we have n different views but parallel data is only available between each of these views, and a pivot view.

there is no parallel data available between the non-pivot views.


Note that unlike the objective function of Cor-rNet (Chandar et al., 2016), the objective function of Equation 4, is a dynamic objective function which changes with each training instance.


Since we need such reference captions for evaluations, we create a new train/valid/test of this dataset.

we take 80K images from the standard train split and 40K images from the standard valid split.


For the task of retrieving images given a caption, we simply reverse the role of the captions and images.

each of the 5000 captions is treated as a query and the 1000 images are treated as documents.


We propose a new variant of the Lesk algorithm to disambiguate the verb sense that is depicted in an image.

we explore the effectiveness of textual, visual and multimodal representations in conjunction with Lesk.


Once we have images for every sense, we can turn these images into feature representations us-ing a convolutional neural network (CNN).

we used the VGG 16-layer architecture (VG-GNet) trained on 1.2M images of the 1000 class ILSVRC 2012 object classification dataset, a subset of ImageNet (Simonyan and Zisserman, 2014).


Table 2 compares how well our models recover common and syntactically interesting supertags.

the Forward and Backward models, motivate the need for a Bi-directional approach.


In this research, we aim to better understand how CWS benefits the subsequent NLP tasks, using semantic slot filling in spoken language understanding (SLU) and named entity recognition (NER) as two case studies.

we investigate the impact of Chinese word segmentation in three different situations.


To achieve this, we propose a joint inference approach that allows information flow among the three local models and finds globally-optimal assignments of all variables, including the trigger variables t, the argument role variables r, and the entity variables a.

we define the following objective: The first term is the sum of confidence scores for individual event mentions based on the parameter estimates from the within-event model.


At time i, G arg i summarizes the argument roles that the entity mentions has played with some event in the past.

g   Denote the given trigger subtypes and argument roles for W in the training time as T = t * 1 , t * 2 , .


This section evaluates the effectiveness of the memory vector and matrices presented in Section 3.2.3.

we test the joint model on different cases where the memory vector for triggers G trg and the memory matrices for arguments G arg/trg and G arg are included or excluded from the model.


The most important observation from the table is that JRNN significantly outperforms all the other methods with large margins when the input sentences contain more than one events (i.e, the row labeled with 1/N in the table).

jRNN is 13.9% better than DMCNN on trigger labeling while the corresponding improvement for argument role labeling is 6.5%, thereby further suggesting the benefit of jRNN with the memory features.


The application of neural networks to EE is very recent.

nguyen and Grishman (2015b) study domain adaptation and event detection via Cnns while Chen et al.


We view the probability computation of a dependency tree as a generation process.

we assume dependency trees are constructed top-down, in a breadth-first manner.


This process can be also viewed as adding an edge and a node to a tree.

lSTMs GEN-l and GEN-R are used to generate the first left and right dependent of a node (w 1 and w 4 in Figure 3).


The four classifiers take the previous hidden state H [:,t ] and the output embedding of the current node W ho • e(w t ) as features.

6 we use a trained LDTREELSTM to go through the training corpus and generate hidden states and embeddings as input features; the corresponding class labels (true and false) are "read off" the training dependency trees.


As a result, the model can act as both a discourse relation classifier and a language model.

: • If trained to maximize the conditional likelihood of the discourse relations, it outperforms state-of-the-art methods for both implicit discourse relation classification in the Penn Discourse Treebank  and dialog act classification in Switch-board (Kalchbrenner and Blunsom, 2013).


The phonetic units that exhibit conventional meaning regularities of this latter kind are called phonesthemes and are the focus of the present paper.

we investigate two main questions: (1) how can the existence of phonesthemes be tested at a large scale by means of a data-driven method?


Phonesthemes are traditionally distinguished from morphemes in being non-compositional.

unthinkable can be thought of as being composed of morphemes un-(meaning not), think, and -able (meaning capable of ), all contributing to the overall meaning of "incapable of being thought" and susceptible of being combined with other units with predictable semantic effects (e.g., un-drinkable, think-er).


We will refer to these units as "candidate phonesthemes" because they all have been considered phonesthemes by previous qualitative studies and our aim is to investigate whether their alleged phenesthemic status is warranted quantitatively.

we focus on two-consonant units in word-initial position, which we will often call prefixes.


We find, in line with previous computational and psycholinguistic studies, that words that share certain phonetic prefixes without being morphologically related are more semantically similar that would be expected by chance.

our phonestheme validation procedure is stricter compared to previous work since we use sets of words that share a random two-consonant prefix as baseline and, importantly, take into account morphological relatedness.


We compare our proposed methods with McDonald (2006).

we use an adapted version described in Filippova et al.


(2013), independently of the extra visual information we exploit.

it could be that MSG has simply learned to treat, say, the lamb visual vector as an arbitrary signature, functioning as a semantically opaque ID for the relevant object, without exploiting the visual resemblance between lamb and sheep.


Secondly, using max-margin training, only training samples from support vectors generate non zero errors.

model training can be sped up by skipping the weight updating for non-support vector training samples.


8and 9, θ represents the weights in RSVMs.

the weights W , A, U and V are updated using sequence level mini-batch method.


To ensure a fair comparison, we employed the same settings for all NMT systems.

except for the maximum sequence length (seqlen, which was to 80), and the stopping iteration which was selected using development data, we used the default settings set out in (Bahdanau et al., 2014) for all NMT-based systems: the dimension of word embedding was 620, the dimension of hidden units was 1000, the batch size was 80, the source and target side vocabulary sizes were 30000, and the beam size for decoding was 12.


In this paper, we try to put two measures together.

if two entities occurred nearby, they can belong to the same template; if they have similar meaning, they can also belong to the same template.


The TransE model represents each relation r by a translation vector r ∈ R k , which is chosen so that h + r ≈ t. The primary contribution of this paper is that two very simple relation-prediction models, SE and TransE, can be combined into a single model, which we call STransE.

we use relationspecific matrices W r,1 and W r,2 as in the SE model to identify the relation-dependent aspects of both h and t, and use a vector r as in the TransE model to describe the relationship between h and t in this subspace.


Specifically, we use relationspecific matrices W r,1 and W r,2 as in the SE model to identify the relation-dependent aspects of both h and t, and use a vector r as in the TransE model to describe the relationship between h and t in this subspace.

our new KB completion model STransE chooses W r,1 , W r,2 and r so that W r,1 h + r ≈ W r,2 t. That is, a TransE-style relationship holds in some relation-dependent subspace, and crucially, this subspace may involve very different projections of the head h and tail t. So W r,1 and W r,2 can highlight, suppress, or even change the sign of, relation-specific attributes of h and t. For example, for the "purchases" relationship, certain attributes of individuals h (e.g., age, gender, marital status) are presumably strongly correlated with very different attributes of objects t (e.g., sports car, washing machine and the like).


Moreover, the SP-based model is much more compact than the alternatives, making its training an order of magnitude faster.

we train several versions of the w2v-SG model, each with a different context type, and evaluate the resulting word embeddings on the task of predicting the similarity scores of the verb and adjective portions of SimLex999.


While in open classification, we allow the classifier to predict labels/classes from the set of C 1 , C 2 , …, C m , C m+1 classes, where the (m+1) th class C m+1 represents the unknown which covers documents of all unknown or unseen classes or topics.

every test instance may be predicted to belong to either one of the known classes y i ∈ , or C m+1 (unknown).


Due to learning in the similarity space with similarities as features, CBS learning generates a boundary based on similarities to separate the positive and negative training data in the similarity space, which is essentially a ball encompassing the positive training data in the original document space.

instead of explicitly minimizing the positively labeled open space risk, CBS learning approximates the radius !


For this baseline (Scheirer et al., 2013), we use all the default parameter settings in the original paper.

the near and far plane pressures are set at !


We can see that in both sets of experiments, the precision and F1 score on the unchanged known classes (comp.windows.x and rec.motorcycles) are better when a more similar class (closer in distance) is selected in training.

comp.windows.x achieves the best result when comp.os.ms-windows.misc is the second known class, and rec.motorcycles achieves the best result when rec.autos is the second known class.


In this paper, we proposed to study the problem of multiclass open text classification.

we investigated the problem via reducing the open space risk, and proposed a solution based on cen-    ter-based similarity space learning.


We then embedded this information in a 1, 000×526 weight matrix between the final hidden and output layers.

we treated 252 neurons in the final hidden layer as dedicated neurons in weight initialization.


We re-implemented the approach by Pichotta and Mooney (2014) with the exception that we use v(e subj , e dobj , e iobj ) instead of v(e subj , e obj , e prep ) to represent events.

we do not model prepositional arguments of an event but discriminate between direct and indirect objects of verbs.


Within this context, we are interested in the task of predicting semantic tuples for images.

given an input image we seek to predict what are the events or actions (referred here as predicates), who and what are the participants (referred here as actors) of the actions and where is the action taking place (referred here as locatives).


After having described the type of scoring functions we are interested in, we now turn our attention to the learning problem.

given a training set D = { x y } of pairs of inputs x and output sequences y we need to learn the parameters {W } and {Z}.


That is, given a training set D = { x y } of pairs of inputs x and output sequences y we need to learn the parameters {W } and {Z}.

for this purpose we will do standard max-likelihood estimation and find the parameters that minimize the conditional negative log-likelihood of the data in D. we will find the {W } and {Z} that mini-mize the following loss function L(D, {W }, {Z}): − x y ∈D logP (y|x; {W }, {Z}) Recall that we are interested in learning low-rank unary and binary potentials.


They showed that the task is much more challenging for QE models trained independently when training data for each annotator is scarce.

sufficient data needs to be available to build individual models for all possible translators.


We attempt to capture the shape in which the ideas flow in the discussion.

we are interested in how many ideas are discussed, how widely they are adopted, who tends to introduce them, and how.


Balance in idea contributions between the team members is a good indicator of productive discussions.

in the best teams (the ones that outperform the best player, i.e., c best > 0) the most idea-prolific player introduces fewer ideas, on average, than in the rest of the games (Figure 5a, p = 0.01).


Further improving classification performance on such a difficult task will hinge on better conversation processing tools, adequate for the domain and robust to the informal language style.

we plan to develop and evaluate models for idea flow and (dis)agreement, using more advanced features (e.g., from dependency relations and knowledge graphs).


Many cognitive theories of conceptual organisation assume that concepts are distributed representations over semantic primitives, often referred to as features or properties 1 (Tyler et al., 2000;Randall et al., 2004).

we can understand the meaning of a concept through its properties.


Ceiling: These rows show the performance of title candidate generation.

the numbers indicate the percentage of mentions that have the gold title in its candidate set, therefore upper-bounds the ranking performance.


This is due to the difficulty of the datasets.

there is less ambiguity because the number of articles in the corresponding Wikipedia is small.


We can see that Local Context and Other Mentions are very effective for most of the languages.

on hard mentions, the performance gain of the three feature groups is from almost 0 to around 50.


They treat this problem as a cross-lingual information retrieval problem.

given the context words of the target mention in the foreign language, retrieve the most relevant English Wikipedia page.


The model jointly considers within language co-occurrence and meaning equivalence across languages.

the monolingual objective for each language is also included in their learning objective.


By contrast, we use |Σ x | + 1 states, where each state records the most recent output character (initially, a special "beginning-of-string" symbol $).

the state label h is the "history" output character immediately before the edit s : t, so the state label h is the history before the next edit, namely the final character of ht.


x t ∈ R l is an input vector of length l. The most common approach is to use an RNN such that: where h t ∈ R n is a hidden state at time t, and f is generally a non-linear transformation, producing e := h T +1 as the input representation.

the decoder is trained to predict the next output y t given the encoded input vector e and all the previously predicted outputs the decoder defines a probability over the output sequence t by decomposing the joint probability into ordered conditionals: With a decoder RNN, we can first obtain the hidden layer at time t as: s t = g(s t−1 , {e, y t−1 }) and feed this into a softmax layer to obtain the conditional probability as: where, In recent work, both f and g are generally LStMs, a kind of RNN which we describe next.


Compounding represents one of the most productive word formation types in many languages.

germanic languages (e.g., german or Dutch) show high productivity in closed compounding, i.e., in creating one-word compounds such as the german Armutsbekämpfungsprogramm 'poverty elimination program'.


For a listener to recognize the phonological distinctness of the pun and the target, they should preferably differ in a perceptually salient position such as a stressed syllable.

we expect that puns would take advantage of the increased acoustic energy in the onset and nucleus of a stressed syllable and utilize these positions for phoneme changes.


Query (b) is composed of two segments that are syntactically independent, but semantically connected.

the first segment is an interrogative sentence rooted in the word done and the second is a noun phrase which specifies the pronoun it from the first segment.


We give a pseudocode in Algorithm 1 that trains this model in an online fashion using feedback from the loopy belief propagation (LBP) inference algorithm (to be described later in Section 3.1.1).

the marginals from the LBP are used in a stochastic gradient descent (SGD) algorithm, which has the following (minibatch) update rule: where θ t and η t are the model parameters and the learning rate at step t, respectively, and 1 N f (θ t ) is the mean gradient for the minibatch (a thread).


As can be seen from this example, recognizing the NP information on such a dataset presents some additional challenges over conventional NP recognition tasks.

the texts are highly informal and noisy, with misspelling errors and without grammatical structures.


Further, we can relate the output of the pre-selector to the number of records that are selected.

the output p j expresses the extent to which the j-th record should be selected.


For the argument realizations, arguments to the left of c pick up words to their right, and arguments to the right pick up words to their left.

for i < c (R i to the left of c but not next to c), l i is empty and r i contains all words between a i and a i+1 .


This division of labor also allows for differing amounts of human intervention both during training and in the interpretation of actions and bears some resemblance to (Andreas et al., 2016).

we will first present results where the model predicts a fixed semantic interpretation of actions which are easily human interpretable (Encoder + Representation).


Thus, Equation 2 becomes: We now extend the DCLM by leveraging the information at the inter-document level, taking advantage of the structure of the repository -a tree in reddit.

by harnessing the information in documents related to a target document, i.e., its siblings and parent, the LM is expected to contain additional relevant information, and hence lower perplexity.


We exploit this for creating lexicons in which words are annotated with lexical information, e.g., with sentiment.

we create high-coverage lexicons with up to 3 million words (i) for three lexical properties: for sentiment, concreteness and frequency; (ii) for five languages: Czech, English, French, German and Spanish; (iii) for two domains, Twitter and News, in a domain adaptation setup.


Since our new dataset 1 is an extension of the dataset from Wiegand and Ruppenhofer (2015), we adhere to the annotation process proposed in that paper.

the basis of the annotation were online dictionaries (e.g.


Simple morphological features ( §4.4) also seem to be meaningful.

the noun suffix -ity (occurring 132 times in our set of opinion nouns) is indicative of speaker views.


Third, it is unsupervised and does not require seed terms, hand-crafted patterns, or any other labeling efforts.

the algorithm takes a set of reviews on a product (e.g., TV, cell phone) as input and produces aspect clusters as output.


3 The Proposed Approach .., x n } be a set of candidate features extracted from reviews of a given product (e.g., TV, cell phone).

by using a part-ofspeech tagger 1 , nouns (e.g., "battery") and two consecutive nouns (e.g., "battery life") are identified as candidates of explicit features, and adjectives and verbs are identified as candidates of implicit features.


We also add problem-specific constraints that specify which clusters cannot be merged together, but instead of manually creating the cannot-links between specific words, our cannot-link constraints are automatically calculated during the clustering process.

two clusters cannot be merged if they violate any of the three merging constraints: (1) The distance between two clusters must be less than a given value δ (see Algorithm 1).


Due to the lack of robust semantic role-labeling for German, we use dependency relations as a proxy.

we identify agents with the dependency relation subj and patients with the relation obj.)


Similar to §5.1, we encode the joint-paraphrase patterns by their individual dependency relations.

the first example in Table 4 would be represented as the feature objp modifier von objp gegen .


(2) We demonstrate the effectiveness of the resource for extracting relation instances in NELL KB.

it improves the recall of both the supervised-and the unsupervised-verb-to-relation mapping; demonstrating the benefit of semi-supervised learning on unlabeled Web-scale text.


Therefore, we incorporate argument type checking in our EM process to ensure that it maps verbs to relations whose argument types match: • In the E-Step, we make sure that unlabeled instances are only labeled with relations that have the same argument types as the instance and that share some verbs with the instance.

in the E-step we compute P (y i (e m ,e n ) = 1 • In the M-step, we make sure that verbs are only mapped to relations whose argument types match at least one of the instances that co-occur with the verbs in SV O .


In other words, in the E-step we compute P (y i (e m ,e n ) = 1 • In the M-step, we make sure that verbs are only mapped to relations whose argument types match at least one of the instances that co-occur with the verbs in SV O .

in the M-step we compute In the E-step, for each unlabeled instance, given the probabilities over relation labels P (y i (e m ,e n ) = 1 | d (e m ,e n ) ;θ t ), and Subset and M utex constraints 6 , similar to (Dalvi et al., 2015), we use a Mixed-Integer Program (MIP) to produce its bit vector of label assignment as output:ŷ t (em,en) .


Evaluation of our model, DER Network, exhibits better results than previous research (Section 3).

we find that max-pooling of entity representations, which is intended to model the accumulation of information on entities, can drastically improve performance.


We train the proposed multiway, multilingual model on ten language pairs from WMT'15 simultaneously and observe clear performance improvements over models trained on only one language pair.

we observe that the proposed model significantly improves the translation quality of low-resource language pairs.


The next question we ask is the following: in which scenario would the proposed multi-way, multilingual neural translation have an advantage over the existing, single-pair model?

we consider a case of the translation between a low-resource language pair.


We control the amount of the parallel corpus of each pair out of three to be 5%, 10%, 20% and 40% of the original corpus.

we train four models with different sizes of parallel corpus for each language pair (En-De, De-En, Fi-En.)


Originally developed to extract biological relations (Craven and Kumlien, 1999), DS was later extended to extract relations from Wikipedia in-foboxes (Wu and Weld, 2007) and Freebase (Mintz et al., 2009).

distant supervision uses the KB to find pairs of entities E 1 and E 2 for which a relation R holds.


We want workers to stay motivated, so our crowdsourcing system also provides feedback to workers.

workers receive adaptive per-batch message feedback at the end of each batch of questions (every 20 questions) about how well they did on the gold questions in the past batches, how much they have earned so far, and a reminder of the bonus for finishing all 10 batches.


Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple.

a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator.


Even though these translation-based models achieve high performance in knowledge graph completion, they all ignore logical properties of rela-tions.

transitive relations and symmetric relations lose their transitivity or symmetricity in the vector space generated by the translation-based models.


Since they map the entities onto a vector space regardless of the role of the entities, they do not express logical properties of relations such as transitivity and symmetricity.

the vectors of transitive or symmetric relations do not deliver transitivity or symmetricity in the embedding spaces from translation-based embeddings.


There are 483,142 triples in FB15K, and 84,172 (= 47,841 + 36,331) triples among them have a transitive or symmetric relation.

the translationbased embeddings do not express triples precisely for about 17% of triples in FB15K.


The main reason why transitive or symmetric relations are not represented precisely by existing translation-based embeddings is that they ignore the role of entities in embedding them onto a vector space.

when a triple (h, r, t) is given, h and t plays different roles.


Then, the entity vectors in the entity space are mapped into the space of r 1 using two mapping matrices M r 1 h and M r 1 t .

head entities are mapped by M r 1 h , while tail entities are projected by M r 1 t .


TransD maps entity vectors into different vectors in relation spaces according to entity and relation types.

the entity vectors are mapped by entity-relation specific mapping matrices.


In addition, its Hits@10 exceeds those of TransH and TransR.

even if TransH and TransR were proposed to tackle the problem of TransE, the proposed lppTransE solves the problem better than TransH and TransR.


In general, the goal is incorporating external, holistic knowledge.

for the sentiment composition task that we experiment with in this paper, we leverage two different types of such external knowledge: (1) sentiment of words and ngrams holistically learned from external, larger corpora, and (2) sentiment of words and phrases from human prior, i.e., annotation assigned by human subjects.


We then use a non-linear feature embedding h c to map a mention x n to a vector-space representation.

we define where W c and b c are parameters of the embedding.


In this section, we describe the features we use to represent mentions.

as shown in Table 1, we use different features under different resolution modes.


Our model outperforms the three baseline systems on all the evaluation metrics.

our model achieves improvements of 2.93% and 3.01% on CoNLL F1 score over the Stanford system, the winner of the CoNLL 2011 shared task, on the CoNLL 2012 development and test sets, respectively.


Our results suggest that it is worthwhile to carefully choose the right type of word embeddings for an extrinsic NLP task, rather than rely on intrinsic benchmark results.

picking the optimal context type and dimensionality is critical.


In the case of dependency-based word embeddings, the context elements are the syntactic contexts of the target word, rather than the words in a window around it.

following Levy and Goldberg (2014), we first 'collapsed' prepositions (as implemented in word2vecf).


We show that while the best practices for setting skipgram hyperparameters typically yield good results on intrinsic tasks, success on extrinsic tasks requires more careful thought.

we suggest that picking the optimal dimensionality and context type are critical for obtaining the best accuracy on extrinsic tasks and are typically task-specific.


Therefore, we conclude that the gains we obtained through interlocking the phrases, could not have been obtained by simply increasing the amount of searching performed by the baseline system.

the interlocking method is introducing novel and useful search steps into the search space.


the scores y k A , y k B provided by the evaluator for the same pair of translations.

we considered an agreement when rankings were in order, e.g.ŷ k A > y k B ⇐⇒ y k A > y k B .


One question that remains to be answered is whether these features could be used as a whole to evaluate the quality of a translation semi-automatically.

whether we can use the gaze information, and other lexical information to anticipate the score that an evaluator will assign to a translation.


In this work, we show that the labeling procedure, when optimized with recent advanced techniques in parsing, can achieve very high speed and accuracy.

our approach employs the recent distributional representation learning technique for parsing.


Pruning We prune unlikely labels by simply exploiting the part-of-speech (POS) tags of the head and the modifier.

let 1(pos h , pos m , l) denote whether there is an arc h l → m in the training data such that h's POS tag is pos h and m's POS tag is pos m .


Despite the simplicity of the decoupled parsing procedure, our labeling model achieves LAS performance on par with the state-of-the-art neural network parser.

our model obtains the best LAS on 5 out of 7 languages, while the neural parser outperforms ours on Catalan and Chinese.


We demonstrate that decoupling these two steps yields both computational gains and improvement in labeling accuracy.

we demonstrate that our labeling model can be used as a post-processing step to improve the accuracy of state-of-the-art parsers.


However, these mistakes only affect the precision in extracting ATFs rather than the recall.

we can build a corpus that considers most cases of additional information.


In other words, we can build a corpus that considers most cases of additional information.

we design the following simple heuristic: let F i and F j be the largest not aligned (possibly discontinuous) word sequences appearing in the sentence pair (S i , S j ), where F i ∈ S i and F j ∈ S j .


Interestingly, the data can be automatically labeled exploiting the MSRP annotation: given a sentence pair from MSRP, we (i) extract the ATF from it and (ii) automatically annotate it as ancillary if the pair is a paraphrase and not ancillary otherwise.

an ATF is considered ancillary only if it is extracted from a paraphrase pair.


we used our best kernel com derived in the work above: c log(e cx 1 + e cx 2 ), and SPTK is the Smoothed Partial Tree Kernel (Croce et al., 2011).


However, the 1-best result of SMT system is not always the best result because the scoring is conducted only with local features.

n-best (n > 1) results may be better than the 1-best result.


The greedy method to generate subset D of D is as follows: we iteratively add a problem p from D \ D to D which minimizes the value of f lex (p, D ).

at each step, we add a problem which has the least average pairwise lexical overlap with the problems already in D .


The greedy method to generate subset D of D is as follows: we iteratively add a problem p from D \ D to D which minimizes the value of f tmpl (p, D ).

the algorithm iteratively chooses problem p from D \ D and adds it to D such that (if possible) p adds a new template to D .


We also compare our classifier against the Excitement Open Platform (EOP) textual entailment classifier (Magnini et al., 2014).

we used the MaxEntClassificationEDA classifier with the RTE-3 pre-trained model which we found to be the best performing one among those available achieving 33% accuracy.


Typically, one would hope to surpass a rescoring approach with decoder integration; however, our lattice implementation fully searches its lattice, even if composition with the word-level language model would cause the lattice to explode in size.

lattice desegmentation has an advantage, as it trades time-efficiency for a perfect search that ignores the complexity introduced by expanded n-gram context.


For our submis- sion trained only on 2014 ESF data, Figures 3 and 4 break down the difference between extracted facts derivable only from supervised systems and those able to be attained from unsupervised systems.

figure 3 measures the number of extracted facts supported by a consensus of the number of systems on the x-axis that also agree with the output of a supervised system.


With the above criteria, we select all correct translation options as positive instances for the revising step, and randomly sample the same number of wrong translation options to be negative instances.

translation options that are used by the baseline system are included as negative instances.


In general, our concept annotation for words is performed in two steps -clustering of context embeddings followed by annotation.

we first employ a clustering algorithm to cluster the context embeddings.


The average number of embeddings for a word is 1.86 for K = 10.

concept annotations do converge as we increase K. Figure 1 presents our proposed workflow to train context-based conceptual word K-embeddings.


It shows that the Kembeddings are improved after certain number of iterations.

for K = 10, we can achieve best performance after 3 to 4 iterations, gaining roughly 1%.


We allow our convolutional neural network to capture the connections between related words in a pair and we feed it with an additional binary-like input about overlapping words (Severyn and Moschitti, 2016).

in the input sentence, we associate an additional word overlap indicator feature o ∈ {0, 1} with each word w, where 1 corresponds to words that overlap in a given pair and 0 otherwise.


The results show that our models outperform the state of the art achieved by more complex networks.

cTKs outperform our cNNs but use more information, e.g., on TREc 13, cTKs obtain an MRR and MAP of 85.53 and 75.18 vs. 77.93 and 71.09 of cNNs.


We introduce several departures from typical architectures on a finer level.

we incorporate adaptive gating in non-consecutive CNNs  in order to focus temporal averaging in these models on key pieces of the questions.


In the purely discriminative setting, we use a maxmargin framework for learning (or fine-tuning) parameters θ.

in a context of a particular training example where q i is paired with p + i , we  where δ(•, •) denotes a non-negative margin.


Their success can be attributed to neural gates that adaptively read or discard information to/from internal memory states.

a LSTM network successively reads the input token x t , internal state c t−1 , as well as the visible state h t−1 , and generates the new states c t , h t : where i, f and o are input, forget and output gates, respectively.


2014, who describe answer reranking experiments on this dataset using a diverse range of features incorporating syntax, lexical semantics and discourse.

they show how discourse information (obtained either via a discourse parser or using shallow techniques based on discourse markers) can complement distributed lexical semantic information.


Finally, the models are much more compact than traditional word-based systems and they are standalone -no processing pipeline is needed.

we do not need a tokenizer to segment text in each of the input languages.


We evaluate our approach on POS tagging using the multilingual universal dependency treebanks (Nivre et al., 2016).

we use English as the source language and test on three Indo-European languages (Danish, German and Spanish) and three non-Indo-European-languages (Finnish, Hungarian and Indonesian).


Instead, we use the directly transferred model to initialize and regularize an unsupervised tagger.

we employ a feature-based HMM (Berg-Kirkpatrick et al., 2010) tagger for both the source and target languages with two important modifications.


For the purposes of comparison (see Section 5), we introduce also a linear transformation without isometry.

we find P that minimizes ||Σ t P − Σ s || 2 via the Moore-Penrose pseudoinverse (Moore, 1920;Penrose, 1955).


In other words, we find P that minimizes ||Σ t P − Σ s || 2 via the Moore-Penrose pseudoinverse (Moore, 1920;Penrose, 1955).


Let θ 0 denote this setting of the parameters.

the unsupervised HMM with initial parameters θ 0 is the direct transfer model.


We use English as the source language and six other languages as targets.

we choose three Indo-European languages: Danish (da), German (de), Spanish (es), and three non-Indo-European languages: Finnish (fi), Hungarian (hu), Indonesian (id).


Baselines We also compare against the prototypedriven method of Haghighi and Klein (2006).

we use the publicly available implementation provided by the authors.


Impact of the Amount of Supervision Figure 2 shows the accuracy of the Direct Transfer, Transfer+EM models, and prototype baseline with different amounts of supervision in German.

the x-axis is the number of translation pairs or prototypes used as supervision.


The intuition of FEMA is similar to SCL and other prior work: it relies on co-occurrence statistics to link features across domains.

fEMA exploits the tendency of many NLP tasks to divide features into templates, and induces feature embeddings by using the features in each template to predict the active features in all other templates -just as the skipgram model learns word embeddings to predict neighboring words.


We include two baseline systems for POS tagging: a classification-based support vector machine (SVM) tagger and a bidirectional maximum entropy Markov model (MEMM) tagger.

we use the L 2 -regularized L 2 -loss SVM implementation in the scikit-learn package (Pedregosa et al., 2011) and L 2 -regularized bidirectional MEMM implementation provided by Stanford CoreNLP (Toutanova et al., 2003;Manning et al., 2014).


Conceptually, this is the opposite of the tree model, as illustrated in Figure 2.


As APiCS did not mark creoles, we used the sociolinguistic feature "Ongoing creolization of pidgins" as a criterion to select creoles.

we filtered out languages whose feature value was neither "Not applicable (because the language is not a pidgin)" nor "Widespread."


It handled missing values using multiple correspondence analy-sis (MCA).

we used the imputeMCA function to predict missing feature values.


figure 5(c) is directly comparable to figure 4.


Table 4 lists the top-10 feature type-value pairs that were derived from the restructurer.

we stochastically removed the influence of the lexifiers and substrates from creole data.


We observe notable differences in approaches depending on the nature of the evaluation metric.

deeper or more complex models (which require greater time and resources to train) generally perform best in the supervised setting, whereas shallow log-bilinear models work best on unsupervised benchmarks.


In particular, deeper or more complex models (which require greater time and resources to train) generally perform best in the supervised setting, whereas shallow log-bilinear models work best on unsupervised benchmarks.

skipThought Vectors (Kiros et al., 2015) perform best on the majority of supervised evaluations, but sDAEs are the top performer on paraphrase identification.


2015assume their sense graph to be an ontology, this graph can be based on any inventory of word-sense and sense-sense relationships.

given a parallel corpus, we can follow the tradition of translation-as-sense-annotation: the senses of an English word type can be defined by different possible translations of that word in another language.


Using their annotated corpus, S&G presented initial results on simplified versions of the ACI and RI tasks (Stab and Gurevych, 2014b).

they applied their learned ACI classifier to classify only gold argument components (i.e., text spans corresponding to a Major Claim, Claim, or Premise in the gold standard) or sentences that contain no gold argument components (as non-argumentative).


Motivated by this weakness, we examine in this paper argument mining in persuasive student essays in a considerably more challenging setting than that of S&G: the end-to-end setting.

we perform argument mining on raw, unannotated essays.


Given the difficulty of clause-level argument component location identification, recent argument mining works that attempted argument component typing and relation identification are not end-toend.

they simplified the task by assuming as input gold argument components (Stab and Gurevych, 2014b;Peldszus and Stede, 2015).


We perform joint inference over the outputs of the ACI and RI classifiers by designing and enforcing within-task and cross-task constraints in the ILP framework.

we create one ILP program for each test essay, as described below.


Using exact matching, the differences in scores between OUR system and BASE's are smaller and highly significant with respect to a smaller number of measures.

under Exact matching OUR system's performances on the RI-P and RI-R metrics are significant (p < 0.02), while under Approx matching, they are highly significant (p < 0.002).


Each row of the table shows the results of one ablation experiment on the test set.

we obtain them by removing exactly one feature set or improvement type from our system.


We propose a solution to the outlined issues.

we follow the idea of distant supervision to construct a large-scale corpus of text segments from diverse domains and registers annotated with respect to argumentativeness.


The work that is most related to ours is the proposal of a method to exploit debate portals for semisupervised argumentation mining by Habernal and Gurevych (2015).

the authors use word embedding techniques for projecting the texts from debate portals into an annotated argument space, relying on the argument model of Toulmin (1958).


Thus, mapping text segments from debate portals to classes for argumentation mining is a promising instance of distant supervision.


In this regard, we propose a "PageRank for arguments" based on the link network of support and attack relations between arguments.

given robust algorithms to identify arguments and their relations across web pages (e.g., via distant supervision), we could build an argument graph for the web.


In this paper, we investigate how to achieve robust performance for argumentation mining across domains, focusing on the classification of the argumentativeness of text segments.

we approach the data side of this problem, namely, we apply distant supervision to automatically create a large annotated corpus with argumentative and non-argumentative text segments from several domains, exploiting metadata from the online debate portal idebate.org.


We characterise each speaker in each debate based on the content and emotion cues he/she generated.

we analyse each candidate in three dimensions: i) what they said (content features); ii) the persuasiveness of the language they used including persuasive argumentation features and emotive language; iii) and external emotions evoked during the debates.


For content and alliteration features, we first removed stopwords.

for computing unigram features we also stemmed words using a Porter stemmer (Porter, 1997).


We find that persuasive argumentation features alone provide improvement upon the unigram baseline.

in terms of nDCG and nDCG-3 7 , the premise and support relation types provide the best results for this feature category.


These results suggest the relevance of "what they said", the " persuasiveness style of their arguments" and the relative importance given by others by means of mentions are good predictors of influence ranking in political debates.

when combining the lexical content of candidates' discourse with their persuasive argumentation style, our results indicate that candidates with higher influence ranking tend to present more premises while clearly stating their stance (i.e.


In this paper, we have studied the impact of argumentation in speaker's discourse and their effect in influencing an audience on supporting their candidature.

we have conducted the study in the domain of political debates.


Our experimental results on the 20 debates for the Republican primary election show that when combined with word deviations and mention percentages,most persuasive argumentation features give superior performance compared to the baselines.

with the Premise and SupportRel types appear to be better predictors of a speaker's influence rank.


Existing works on the analysis of writing revisions (Adler et al., 2011;Bronner and Monz, 2012;Daxenberger and Gurevych, 2013;Zhang and Litman, 2015) typically compare two versions of a text to extract revisions, then classify the purpose of each revision in isolation.

while limited contextual features such as revision location have been utilized in prior work, such features are computed from the revision being classified but typically not its neighbors.


This LSTM is able to place more emphasis on important word-level matching results.

we observe that this LSTM remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label.


Furthermore, through further analyses of the learned parameters, we show that the mLSTM architecture can indeed pick up the more important word-level matching results that need to be remembered for the final prediction.

we observe that good wordlevel matching results are generally "forgotten" but important mismatches, which often indicate a contradiction or a neutral relationship, tend to be "remembered."


Let us use a k to denote such an attention vector for word x t k in the hypothesis.

a k is defined as follows 1 : where α kj is an attention weight that encodes the degree to which x t k in the hypothesis is aligned with x s j in the premise.


At each position the LSTM takes in both a k and h t k as its input.

figure 1 gives an overview of our model in contrast to the model by Rocktäschel our model works as follows.


We use the concatenation of a k , which is the attention-weighted version of the premise for the k th word in the hypothesis, and h t k , the hidden state for the k th word itself, as input to the mLSTM.

let us define We then build the mLSTM as follows: With this mLSTM, finally we use only h m N , the last hidden state, to predict the label y.


This is inspired by common practice in machine translation.

we introduce a vector h s 0 , which is fixed to be a vector of 0s of dimension d. This h s 0 represents NULL and is used with other h s j to derive the attention vectors {a k } N k=1 .


We also consider a few variations of our model.

the following models are implemented and tested in our experiments: • Word-by-word attention (d = 150): This is our implementation of the word-by-word attention model by Rocktäschel et al.


On the other hand, for Example 2 and Example 3, which are contradiction and neutral, we see generally darker colors.

in Example 2, we can see that the colors are consistently dark starting from the word "his" in the hypothesis until the end.


Instead of comparing microbe and bacterium directly with their PPDB 2.0 score, we look up all of the paraphrases of microbe and all of the paraphrases of bacterium, and compare those two lists.

we form notional word-paraphrase feature vectors v p i and v p j where the features correspond to words with which each is connected in PPDB, and the value of the k th element of v p i equals P P DB 2.0 Score(i, k).


multiplying each pairwise entry by its entailment probability.

we set where p ind (i, j) gives the PPDB 2.0 probability that there is an Independent entailment relationship between words i and j.


Our Contribution: We propose an extension of the distributional idea for unsupervised iSRL to loosen the need for annotated training data.

we propose to induce predicate and role-specific prototypical fillers from large amounts of SRL annotated texts in order to resolve null instantiations as (semantically and syntactically) similar elements found in the context.


Beyond SRL annotations in a strict sense, this might even extend to syntactic dependency annotations that are occasionally taken as a substitute for semantic roles proper.

we see potential in combining our experiments with on-going efforts to cross-lingual projection, adaptation and harmonization of syntax annotations along the lines of Chiarcos (2014, 2016) and related approaches based on frameworks such as the Universal Dependencies (Nivre, 2015, UD).


Hence, we introduce attention mechanism to extract such words that are important to the meaning of the sentence and aggregate the representation of those informative words to form a sentence vector.

that is, we first feed the word annotation h it through a one-layer MLP to get u it as a hidden representation of h it , then we measure the importance of the word as the similarity of u it with a word level context vector u w and get a normalized importance weight α it through a softmax function.


Subfigures 3(b)-(f) in Figure 3 and Figure 4 correspond to the rating 1-5 respectively.

figure 3(b) shows that the weight of good concentrates on the low end in the reviews with rating 1.


The nonconsecutive feature interaction is well addressed by the memory cell and different gates in LSTM unit.

the forget gate is able to decay the information according to the context rather than a fixed decaying weight in tensor product based CNNs.


These observations motivate us to construct a textual modeling architecture that captures long-term dependencies without relying on parsing for both sentence and document inputs.

we propose Dependency Sensitive Convolutional Neural Networks (DSCNN), an end-to-end classification system that hierarchically builds textual representations with only root-level labels.


Experimental results demonstrate the effectiveness of our approach comparable with the state-of-the-art.

our method achieves highest accuracies on MR sentiment analysis (Pang and Lee, 2005), TREC question classification (Li and Roth, 2002), and subjectivity classification task SUBJ (Pang and Lee, 2004) compared with several competitive baselines.


In unordered models, textual representations are independent of the word order.

ignoring the token order in the phrase and sentence, the bag-of-words model produces the representation by averaging the constituting word embeddings (Landauer and Dumais, 1997).


One-dimensional wide convolution computes the feature map c of length for the input sentence.

in wide convolution, we stack h t column by column, and add (l − 1) zero vectors to both ends of the sentence respectively.


We propose an augmentation of MG-CNN, Multi-Group Norm Constraint CNN (MGNC-CNN), which differs in its regularization strategy.

in this variant we impose grouped regularization constraints, independently regularizing subcomponents o l derived from the respective embeddings, i.e., we impose separate max norm constraints λ l for each o l (where l again indexes embedding sets); these λ l hyper-parameters are to be tuned on a validation set.


We go beyond this by suggesting that eye-tracking recordings can be used to induce better models for sentence compression for text simplification.

we show how to use existing eye-tracking recordings to improve the induction of Long Short-Term Memory models (LSTMs) for sentence compression.


We instead train deep bi-LSTMs to solve additional tasks to sentence compression, namely CCG-tagging and gaze prediction, using the additional tasks to regularize our sentence compression model.


, w l } of fixed size l that contains mentions (either directly or by coreference) to both c 1 and c 2 .

s c 1 ,c 2 includes the text of every scene, chronologically ordered, in which c 1 and c 2 are present together.


This is a ranking problem; as in the rest of our approach, we solve it using standard neural machinery.

we produce an LSTM representation of the question, a feature-based representation of the query, and pass both representations through a multilayer perceptron (MLP).


(ii) Based on the learned feature representations from Bi-LSTM, two conditional random field (CRF) models are performed for sequence labeling for source and target domain separately, where parameter transfer learning is performed.

an upper bound of KL divergence between the source and target domain's CRF label distributions is added over the emission and transition matrices across the source and target CRF models to explore the shareable parts of the parameters.


In practice, c i is generated by truncating the original context with words beyond the context window size C both to the left and to the right of m i .

we compute a probability distribution over all the K = |Y| types in the target type hierarchy Y.


However, predicting other high level types like location or organization would be inappropriate.

we want the loss function to penalize less the cases where types are related.


Aside from the advantages brought by adopting the single label classification setting, we can see one disadvantage of this setting based on Table 2.

the performance upper bounds of our proposed model are no longer 100%: for example, the best strict accuracy we can get in this setting is 88.28% for FIGER(GOLD).


We must sum over all N n such labelings a ∈ A(n , N ) since the true labeling is not observed.

we sum over all ways a of completing the data for language .


Distances in this latent space are used to compute the dissimilarity of phones for modeling dispersion, and also to describe the phone's ability to vary across languages.

two phones that are distant in the latent space can appear in the same inventory-presumably they are easy to discriminate in both perception and articulation-and it is easy to choose which one better explains an acoustic measurement, thereby affecting the other measurements that may appear in the inventory.


Note that this implicitly samples the language's phone inventoryV (a), which is fully determined by a.

we employ an MCMC method closely related to Gibbs sampling.


In order to make the model location-aware, the attention mechanism takes into account the previous attention weight vector α t−1 .

we use the attention mechanism proposed by Chorowski et al.


In addition, previous work has reserved sw4154 to sw4500 for "future use" (dev2), but we added this set to our training set.

all of our models are trained on Switchboard conversations sw2000 to sw3000 as well as sw4154 to sw4500.


In figures 6, 7, and 8, we follow node correction notations as in (Kummerfeld et al., 2012).

missing nodes are marked in blue on the gold tree, extra nodes are marked red in the predicted tree, and yellow nodes denote crossing.


We evaluate our model on discussions from the Change My View forum on Reddit, where users (OHs) post their views on various issues, partic-ipate in discussion with challengers who try to change the OH's view, and acknowledge when their views have been impacted.

we aim to answer the following questions: • RQ1.


Interaction summary (Figure 3 (D)) Next, for each of the OH's sentences, the model summarizes what types of meaningful interaction occur with the comment's sentences.

given all interaction embeddings for the OH's ith sentence, Intuitively, max pooling is to capture the existence of an interaction and its highest intensity for each of the OH's sentences-the interaction does not have to occur in all sentences of the comment.